Long non-coding RNA TRPM2 antisense RNA as a potential therapeutic target promotes tumorigenesis and metastasis in esophageal cancer
Bibliographic record
Abstract
Esophageal cancer (EC) is one type of aggressive gastrointestinal cancers. The treatment of EC is challenging. Effective therapeutic targets require development. Long non-coding RNA TRPM2 antisense RNA (LncRNA TRPM2-AS) is considering a novel biomarker and therapeutic target for various types of cancer. However, the role of lncRNA TRPM2-AS in EC remains unknown. This study aimed to illustrate effects of LncRNA TRPM2-AS on EC growth and metastasis and potential underlying molecular mechanisms. LncRNA TRPM2-AS expression was determined in both EC tissues and cell lines by quantitative real-time polymerase-chain reaction (qRT-PCR). Cell proliferation ability was evaluated by cell counting kit-8 and colony formation assays. Cell apoptosis was analyzed by flow cytometry. Cell migration and invasion were determined using transwell. Epithelial–mesenchymal transition (EMT)-related markers expression were determined using qRT-PCR and Western blotting. Furthermore, potential lncRNA TRPM2-AS targeting miRNAs were predicted by public databases. The expression of five selected miRNAs were validated by qRT-PCR. We found that lncRNA TRPM2-AS expression was increased in EC tissues and cell lines compared with respective control. Silencing lncRNA TRPM2-AS suppressed EC cell proliferation, migration, and invasion while promoted cell apoptosis. Moreover, lncRNA TRPM2-AS knockdown reduced neural cadherin, vimentin, and matrix metallopeptidase 9 gene and protein expressions while increased epithelial cadherin expression. Furthermore, lncRNA TRPM2-AS knockdown promoted microRNA (miR)-1291, miR-6852-5p, and miR-138-5p expressions. Taken together, this study for the first time demonstrates that upregulation of lncRNA TRPM2-AS in EC promotes the growth and metastasis of EC likely through interacting with miR-1291, miR-6852-5p, and miR-138-5p.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".